The potential value within the manufacturing industry for machine vision systems with the flexibility and acuity of human sight is widely recognized. Unfortunately, the development of a general purpose vision technology has not been as successful as hoped. No single technology has proven to be capable of handling a significant spectrum of applications. Most available systems function only within selected market niches and perform disappointingly elsewhere.
The machine vision industry has been involved for the last 10 years in one continuing struggle: How to deal with the data in the time available. A vision system works with a digital representation of the scene under investigation. The scene is digitized into an array of numbers which is roughly 256 data elements square. Each 8 bit pixel (picture data element) represents the intensity of the reflected light (256 levels) at each point in the scene. This array of 65,536 numbers is the basic data structure which must be processed by all vision systems to obtain information from the observed scene.
The objective for any vision system is to process the pixels in the image array in such a manner as to separate the object of interest from the background and the noise. Difficulty arises when a classic serial computer is applied to doing operations on such a large block of data. Most popular 16 to 32 bit micro-computers require 10 to 15 seconds to perform a simple noise reducing filter operation. This amount of processing time is totally unacceptable for most industrial tasks. As a result, a major objective of those who have sought to deal with industrial vision problems has been to reduce or simplify the image data.
The industry to date has been dominated by 3 basic schemes: binary processing, structured light and correlation. Each method illustrates an approach to reducing the data.